It will be appreciated that a personalised item of sales literature, for example a brochure or an advertising flyer, is likely to result in more sales per literature item than an item of sales literature that is sent en masse to many customers. An item of sales literature that is produced for the general public will undoubtedly include information that is not of interest to all customers that receive the literature and each customer will have to look through the literature to find the parts that are of interest to them. Personalising an item of sales literature can include selecting specific content-elements for a specific customer, and placing these in certain areas of a brochure or flyer in an aesthetically pleasing way. Thus, ideally each customer would receive a unique edition of the sales literature.
With the advent of databases holding information about customers', clients', etc. (hereinafter referred to as customer) preferences, purchases, past actions, and the like, it has become possible to generate targeted communications that are targeted specifically to a particular customer based upon the data held in the database. Such databases may be exemplified by so-called supermarket loyalty schemes. However, the skilled person will appreciate that such schemes are only one such example.
It will be appreciated that such databases may comprise many thousands (and even tens, hundreds of thousands or more) of customers. It would be impossible to check, manually, that targeted communications sent to such a number of people conform to accepted presentation rules within reasonable time scales. As such the process should be advantageously performed automatically.
Elements for the communication may be graphical or textual in nature and different elements may have logical connections with each other or even be part of one another. For example, a furniture suite and a settee from the same suite may be sold separately. Thus, the suite and the settee may be considered separate but may need to be placed logically together. The elements for inclusion in any one catalogue are therefore highly variable which increases the difficulty in laying out the communication.
Further, elements for inclusion in the communication may vary from one item to the next. For example, a picture may be provided for each of the settee and the suite. It is likely that the size of the picture, its aspect ratio and the like will vary. Thus elements may not necessarily be switched with one another without the need to re-work the layout for the communication. Thus, prior art solutions such as hard templates are problematic in that they cannot generally handle elements which vary from one to another.
For example, in each edition of the communication, images for consideration at any one point may have significantly different aspect ratios, scalability ranges, and/or may be missing. Similarly, descriptions may significantly vary in length and also be missing. Hard template technology cannot tolerate such variability: missing data or data that is too small will result in unsightly empty holes in a layout, whilst oversized data will unavoidably create unauthorized overlaps between different fields and items. Neither of these is acceptable for professionally laid out communications. Moreover, items layout by hard templates will always result in a layout of the same dimensions—this will not provide required flexibility for creating highly customized communications on fly; i.e. editions of the same communication targeted to each intended customer.
Known systems for automatically packing content-elements into an allocated area aim to minimize the total area taken up by the content-elements. This approach is not relevant for publishing where aesthetic appeal is a priority, and a number of design rules should be considered. Variable, or smart templates, are also know and provide an extension of hard templates; see for example U.S. application U.S. Ser. No. 10/738,178 filed Dec. 16, 2003.
Other known systems for packing content-elements into an allocated area select content-elements that are to be placed next to each other using random searches based on analogies to other processes, for example simulated annealing. Such searches tend to be in infinite domains and thus may not result in acceptable solutions within a reasonable time scale.